Publications

**Research supported in part by NSF-DMS (2124507), UF Research-Artificial Intelligence Research Catalyst Fund, and UF Informatics Institute COVID-19 SEED Fund**

Li, X., Safikhani, A., Shojaie, A. Estimation of High-dimensional Markov-switching VAR Models with an Approximate EM Algorithm, preprint. [link]

Bai, P., Bai, Y., Safikhani, A., Michailidis, G. Multiple Change Point Detection in Structured VAR Models: The VARDetect R Package, preprint. [link]

Feng, T., Gnanaolivu, R., Safikhani, A., Liu, Y., Jiang J., Chia, N., Partin, A., Vasanthakumari, P., Zhu, Y., Wang, C. (2024) Variational and Explanatory Neural Networks for Encoding Cancer Profiles and Predicting Drug Responses, ICML, AI for Science Workshop: Scaling in AI for Scientific Discovery. [link]

Yin, H., Safikhani, A., Michailidis, G. (2024) A Functional Coefficients Network Autoregressive Model, Statistica Sinica. [link]

Deng, W., Polak, P., Safikhani, A., Shah, R. (2024) A Unified Framework for Fast Large-Scale Portfolio Optimization, Data Science in Science. [link]

Yin, H., Safikhani, A., Michailidis, G. (2023) A General Modeling Framework for Network Autoregressive Processes, Technometrics. [link]

Tepe, E., Safikhani, A. (2023) Spatio-Temporal Modeling of parcel-level land-use changes Using Machine Learning Methods, Sustainable Cities and Society. [link]

Ma, M., Safikhani, A. (2022) Theoretical Analysis of Deep Neural Networks for Temporally Dependent Observations, NeurIPS. [link]

Bai, P., Safikhani, A., Michailidis, G. (2022) Multiple Change Point Detection in Reduced Rank High Dimensional Vector Autoregressive Models, Journal of American Statistical Association (Theory & Methods). [link]

Bai, Y., Safikhani, A. (2022) A Unified Framework for Change Point Detection in High-dimensional Linear Models, Statistica Sinica. [link]

Kim, Y., Safikhani, A., Tepe, E. (2022) Machine Learning Application to Spatio-temporal Modeling of Urban Growth, Computers, Environment and Urban Systems. [link]

Zhang, K., Safikhani, A., Tank, A., Shojaie, A. (2022) Penalized Estimation of Threshold Auto-Regressive Models with Many Components and Thresholds, Electronic Journal of Statistics. [link]

Moghimi, B., Kamga, C., Safikhani, A., Mudigonda, S., Vicuna, P. (2022) Non-Stationary Time Series Model for Station Based Subway Ridership During COVID-19 Pandemic (Case Study: New York City), Transportation Research Record: Journal of the Transportation Research Board. [link]

Bai, Y., Safikhani, A., Michailidis, G. (2022) Hybrid Modeling of Regional COVID-19 Transmission Dynamics in the U.S., IEEE Journal of Selected Topics in Signal Processing. [link]

Bai, P., Safikhani, A., Michailidis, G. (2021) A Fast Detection Method of Break Points in Effective Connectivity Networks, IEEE Transactions on Medical Imaging. [link]

Wang, Z., Safikhani, A., Zhu, Z., Matteson, D. (2021) Regularized Estimation in High-dimensional Vector Auto-Regressive Models using Spatio-Temporal Information, Statistica Sinica. [link]

Safikhani, A., Bai, Y., Michailidis, G. (2021) Fast and Scalable Algorithm for Detection of Structural Breaks in Big VAR Models, Journal of Computational and Graphical Statistics. [link]

Kaul, A., Fotopoulos, S. B., Jandhyala, V. K., Safikhani, A. (2021) Inference on the change point under a high dimensional sparse mean shift, Electronic Journal of Statistics. [link]

Maiti, T., Safikhani, A., Zhong, PS. (2020) On the Uncertainty Estimation in Functional Linear Models, Canadian Journal of Statistics. [link]

Safikhani, A., Shojaie, A. (2020) Joint Structural Break Detection and Parameter Estimation in High-Dimensional Non-Stationary VAR Models, Journal of American Statistical Association (Theory & Methods). [link]

Bai, P., Safikhani, A., Michailidis, G. (2020) Multiple Change Point Detection in Low Rank and Sparse High Dimensional Vector Autoregressive Models, IEEE Transactions on Signal Processing. [link]

Safikhani, A., Kamga C., Mudigonda S., Faghih S.,  Moghimi B.  (2020) Spatio-Temporal Modeling of Yellow Taxi Demands in New York City Using Generalized STAR Models, International Journal of Forecasting. [link]

Safikhani, A., Xiao, Y. (2019) Spectral Conditions for Equivalence of Gaussian Random Fields with Stationary Increments, Electronic Journal of Probability. [link]

Faghih S., Safikhani, A., Moghimi B., Kamga C. (2019) Predicting Short-term Uber Demand in New York City Using Spatio-Temporal Modeling, Journal of Computing in Civil Engineering. [link]

Moghimi B., Safikhani, A., Kamga C., Hao W., Ma, J. (2018) Short-Term Prediction of Signal Cycle on an Arterial with Actuated-Uncoordinated Control Using Sparse Time Series Models, IEEE Transactions on Intelligent Transportation Systems. [link]

Yuan Q., Hao W., Su H., Bing G., Gui X., Safikhani, A. (2018) Investigation on Range Anxiety and Safety Buffer of Battery Electric Vehicle Drivers, Journal of Advanced Transportation. [link]

Wyatt, G., Sikorskii, A., Victorson, D., OConnor, P., Hankin, V., Safikhani, A., Crane, T., Badger, T. (2018) PROMIS and legacy measures compared in a supportive care intervention for breast cancer patients and caregivers: Experience from a randomized trial, Pscyho-oncology. [link]

Moghimi, B., Safikhani, A., Kamga, C., & Hao, W. (2017). Cycle-Length Prediction in Actuated Traffic-Signal Control Using ARIMA Model. Journal of Computing in Civil Engineering. [link]

Wyatt, G. K., Sikorskii, A., Safikhani, A., McVary, K. T., & Herman, J. (2016). Saw Palmetto for Symptom Management During Radiation Therapy for Prostate Cancer. Journal of pain and symptom management. [link]

Safikhani, A., Xiao, Y. (2014) Covariance Tapering for anisotropic nonstationary Gaussian random fields with application to large scale spatial data sets, proceedings of the 11th International Symposium on Spatial Accuracy. [link]